The success of deep learning has catalyzed many extensions of classical learning theory to modern models and algorithms. In particular, the double-descent phenomenon [1]} has inspired a wide breadth of research, in which the classical bias-variance (BV) decomposition has been key [2]}, [3]}. Such analyses have been instrumental to understanding the performance of various strategies that improve deep learning methods, including ensembles of deep networks [4]}, [5]} — a simple method with state-of-the-art robustness and uncertainty results [6]}, [7]}.
